Parent Topic: Supervised Algorithms
Maximum Likelihood
The full maximum likelihood classifier uses the Gaussian
threshold (THRS) stored in each class signature to determine if
a given pixel falls within the class or not. The threshold is
the radius (in standard deviation units) of a hyperellipse
surrounding the mean of the class in feature space. If the
pixel falls inside the hyperellipse, it is assigned to the class.
The class bias (BIAS) is used to resolve overlap between
classes, and weights one class in favour of another. If the
pixel does not fall inside any class, it is assigned to the
null class (code 0).
The maximum likelihood classifier is considered to give more
`accurate' results than parallelepiped classification. However, it
is much slower due to extra computations. We use the word
`accurate' in quotes because this assumes that classes in the
input data have a Gaussian distribution and that signatures were
well selected. This is not always a safe assumption.
Parent Topic: Supervised Algorithms
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